proposed_method/ImageBind-main/run_baseline_v1_train_fully.sh
SEED=123
MODEL_NAME='fine_tune_baseline_v1_train_fully'
N_EPOCH=10
LR=5e-5
TRAIN_BS=32 #! 
VAL_BS=32 #! some val. data may not be used, however this will not significantly affect the model selection
TEST_BS=32
val_data_type='total' 
test_data_type='close' # bs can use 32 for 'close' test data
test_strategy_type='v1' # 'v1' or 'v0' or 'v01', best: v1


# #! Train
EXT="woSA_type_evenLast_K1_wCorrectTAOnlyselfLayer1_woTextTune_sqrtAV" #! (optimal, GEN1 w min-max norm) should carefully modify this to match the config.py
python fine_tune_baseline_v1_train_fully.py \
--lr ${LR} \
--seed ${SEED} \
--model ${MODEL_NAME} \
--snapshot_pref "./ExpResults/${MODEL_NAME}/train_mode_seed${SEED}_bs${TRAIN_BS}_Lr${LR}_evalStrategy_${test_strategy_type}/${EXT}" \
--n_epoch ${N_EPOCH} \
--train_batch_size ${TRAIN_BS} \
--val_batch_size ${VAL_BS} \
--test_batch_size ${TEST_BS} \
--val_data_type ${val_data_type} \
--test_data_type ${test_data_type} \
--test_strategy_type ${test_strategy_type} \
--eval_freq 1 \
--print_iter_freq 100 \
--clip_gradient 0.8



# # # #! Test/Inference
# # EXT="woSA_type_evenLast_K1_wTAOnlyselfLayer1_woTextTune_sqrtAV" #! (optimal, GEN1 w min-max norm) should carefully modify this to match the config.py
# python fine_tune_baseline_v1_train_fully.py \
# --lr ${LR} \
# --seed ${SEED} \
# --model ${MODEL_NAME} \
# --evaluate \
# --snapshot_pref "./ExpResults/${MODEL_NAME}/test_mode_seed${SEED}_bs${TRAIN_BS}_Lr${LR}_evalStrategy_${test_strategy_type}/${EXT}" \
# --resume "./ExpResults/${MODEL_NAME}/train_mode_seed${SEED}_bs${TRAIN_BS}_Lr${LR}_evalStrategy_${test_strategy_type}/${EXT}/task_FullySupervised_best_model.pth.tar" \
# --test_batch_size ${TEST_BS} \
# --test_data_type ${test_data_type} \
# --test_strategy_type ${test_strategy_type} \
# --print_iter_freq 100 \
# --clip_gradient 0.8




